Greater empowerment
Eight of nine respondents reported feeling more empowered after the study.
Plain-language research summary
What happens when people help shape AI adoption?
My doctoral study examined how public-sector professionals experienced generative AI when adoption began with learning, structured experimentation, peer exchange and agency instead of a top-down technology rollout.
The study
People were participants in the change, not just recipients of it.
The study used participatory action research with 11 co-researchers. Participants learned about generative AI, identified real work use cases, experimented with tools, reflected together and helped interpret what changed.
Nine participants responded to the survey measures presented here. The 89% empowerment result describes responses after the study; the 78% regular-use result describes the three-month follow-up.
What the follow-up showed
Continued use
At the three-month follow-up, seven of nine respondents reported using generative AI daily or weekly.
Co-researchers
Eleven professionals participated as co-researchers across the participatory study.
Why it matters
AI adoption is an organizational change challenge.
The findings suggest that organizations should build baseline capability before demanding adoption, create safe room for thoughtful experimentation, involve the people closest to the work, and pair speed with professional judgment. These descriptive results come from a small participatory study; they do not by themselves establish causation or generalize to every organization.
The core lesson is not that every organization should use the same tools. It is that people are more likely to develop useful, durable practices when they have the knowledge and agency to help shape how technology enters their work.